Artificial Odor Discrimination System using electronic nose and neural networks for the identification of urinary tract infection

Current clinical diagnostics are based on biochemical, immunological or microbiological methods. However, these methods are operator dependent, time consuming, expensive and require special skills, and are therefore not suitable for point-of-care testing. Recent developments in gas-sensing technology and pattern recognition methods make electronic nose technology an interesting alternative for medical point-of-care devices. An electronic nose has been used to detect Urinary Tract Infection from 45 suspected cases that were sent for analysis in a UK Public Health Registry. These samples were analysed by incubation in a volatile generation test tube system for 4-5h. Two issues are being addressed, including the implementation of an advanced neural network, based on a modified Expectation Maximisation scheme that incorporates a dynamic structure methodology and the concept of a fusion of multiple classifiers dedicated to specific feature parameters. This study has shown the potential for early detection of microbial ontaminants in urine samples using electronic nose technology

Electronic nose: clinical diagnosis based on soft computing methodologies

Recently, the use of smell in clinical diagnosis has been rediscovered due to major advances in odour sensing technology and artificial intelligence. It was well known in the past that a number of infectious or metabolic diseases could liberate specific odours characteristic of the disease stage and among others, urine volatile compounds have been identified as possible diagnostic markers. A newly developed electronic nose based on chemoresistive sensors has been employed to identify in vitro 13 bacterial clinical isolates, collected from patients diagnosed with urinary tract infections, gastrointestinal and respiratory infections, and in vivo urine samples from patients with suspected uncomplicated UTI who were scheduled for microbiological analysis in a UK health laboratory environment. An intelligent model consisting of an odour generation mechanism, and a classifier system based a neural networks, genetic algorithms, and multivariate techniques such as principle components analysis and discriminant function analysis-cross validation. The experimental results confirm the validility of the presented methods

Interactions between cell surface protein disulphide isomerase and S-nitrosoglutathione during nitric oxide delivery

In this study, we investigated the role of protein disulphide isomerase (PDI) in rapid metabolism of S-nitrosoglutathione (GSNO) and S-nitrosoalbumin (albSNO) and in NO delivery from these compounds into cells. Incubation of GSNO or albSNO (1 μM) with the megakaryocyte cell line MEG-01 resulted in a cell-mediated removal of each compound which was inhibited by blocking cell surface thiols with 5,5′-dithiobis 2-nitrobenzoic acid (DTNB) (100 μM) or inhibiting PDI with bacitracin (5 mM). GSNO, but not albSNO, rapidly inhibited platelet aggregation and stimulated cyclic GMP (cGMP) accumulation (used as a measure of intracellular NO entry). cGMP accumulation in response to GSNO (1 μM) was inhibited by MEG-01 treatment with bacitracin or DTNB, suggesting a role for PDI and surface thiols in NO delivery. PDI activity was present in MEG-01 conditioned medium, and was inhibited by high concentrations of GSNO (500 μM). A number of cell surface thiol-containing proteins were labelled using the impermeable thiol specific probe 3-(N-maleimido-propionyl) biocytin (MPB). Pretreatment of cells with GSNO resulted in a loss of thiol reactivity on some but not all proteins, suggesting selective cell surface thiol modification. Immunoprecipitation experiments showed that GSNO caused a concentration-dependent loss of thiol reactivity of PDI. Our data indicate that PDI is involved in both rapid metabolism of GSNO and intracellular NO delivery and that during this process PDI is itself altered by thiol modification. In contrast, the relevance of PDI-mediated albSNO metabolism to NO signalling is uncertain

Intelligent systems for computer-assisted clinical endoscopic image analysis

The importance of computer-assisted diagnosis in endoscopy is to assist the physician in detecting the status of tissues by characterising the features from the endoscopic image. Due to the complex nature of clinical manifestations, employing a single "feature" technique to detect different abnormalities may not necessarily yield accurate results. In this paper schemes have been developed to extract new texture features from the texture spectra in the chromatic and achromatic domains for a selected region of interest from each colour component histogram of endoscopic images. The implementation of an advanced neural network scheme and the concept of fusion of multiple classifiers dedicated to specific feature parameters have been also adopted in this paper. The endoscopic images used in this study have been obtained using the new M2ATM Swallowable Imaging Capsule - a patented, video colour-imaging disposable capsule. The detection accuracy of the proposed system has reached to 100%, providing thus an indication that such intelligent schemes could be used as a supplementary diagnostic tool in endoscopy